Design of an Exploration-Separated, Contextual MAB Mechanism
Motivated by demand management in smart grids, we propose a novel
exploration separated MAB mechanism to incentivize strategic users to
reduce their demands when there is a shortfall due to grid failures or
time varying supply. The proposed mechanism incorporates realistic
features of the demand response problem. A time varying and non-linear
cost function in this setting throws up non-trivial challenges for
Design of a Low Regret, Combinatorial, Randomized MAB Mechanism
Existing work on mechanisms for stochastic MAB problems deals with
settings where either a single agent needs to be selected at a time or a
subset of top-ranking agents needs to be selected at a time. Our work
non-trivially generalizes this to a situation where a subset of agents
needs to be selected so as to solve a general combinatorial optimization
problem. Motivated by an application in crowdsourcing, we consider a
problem of selecting an optimal subset of workers for each crowdsourced
task so that the outcome obtained after aggregating the labels from the
selected workers guarantees a target accuracy level in a cost optimal way.
Design of a Low Regret, Deterministic MAB Mechanism
There are two broad classes of mechanisms for stochastic MAB problems: (a)
deterministic, which invariably suffer from high regret, and (b)
randomized, which often suffer from high variance in payments. We propose
a deterministic, low regret, Upper Confidence Bound (UCB) based mechanism
by working with a slightly weaker notion of truthfulness which serves the
purpose perfectly for crowdsourcing applications.
Design of a Low Regret, Thompson Sampling based MAB Mechanism
Many existing MAB mechanisms use Upper Confidence Bound (UCB) based
algorithms, which provide a frequentist approach for learning the
parameters of the distributions. We develop, for the first time, a
Thompson sampling based (Bayesian) approach to MAB mechanism design. The
randomized nature of Thompson sampling creates unique challenges for
mechanism design but offers many attractive advantages. The mechanism
proposed has immediate applications to procurement auctions.
August 2011-June 2016. With Prof. Y. Narahari from Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore, Karnataka.
Title: Mechanisms for Stochastic Multi-armed Bandit Problems.
Abstract: The multi-armed bandit (MAB) problem is a widely studied problem in the
machine learning literature, in the context of online learning. The MAB
problem becomes more relevant but at the same time more challenging when
the arms are controlled by strategic agents. Dealing with strategic agents
warrants the use of game theory and mechanism design principles in
conjunction with online learning, and leads to non-trivial technical
challenges. This thesis focuses on designing mechanisms for stochastic
MAB problems where the rewards produced are stochastic. Our work is
motivated by many contemporary, practical applications such as internet
advertising, crowdsourcing, procurement, and smart grids. The work offers
novel mechanisms for stochastic MAB problems in various representative,
generic settings inspired by the above applications. Our contributions
include the following.
August 2008-2010. With Prof. Shirish Shevade from Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore, Karnataka.
Title: An Efficient Algorithm for online SVM Classifier Design
In this work we designed an efficient algorithm for online SVM classifier without losing
accuracy. We designed the concept of a margin band and adapted this margin band according to the incoming data so that the algorithm gives low test error and is efficient both in terms of space and time. We then applied an improved version of SMO algorithm to decide which example falls into the margin band, which is a support vector and which can be discarded.
August 2004-2008. Shri Govindram Seksaria Institute of Technology and Science (SGSITS), Indore, Madhya Pradesh